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We introduce novel convergence results for asynchronous iterations that appear in the analysis of parallel and distributed optimization algorithms.
Chaotic relaxation
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Gérard M. Baudet · 1978
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Dimitri P. Bertsekas · 1983
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Distributed asynchronous relaxation methods for convex network flow problems
Dimitri P. Bertsekas and Didier El Baz · 1987
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Introduction to Optimization
Boris T. Polyak · 1987
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Leslie G. Valiant · 1990
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Doron Blatt, Alfred O. Hero, and Hillel Gauchman · 2007
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Mortada Mehyar, Demetri Spanos, John Pongsajapan, Steven H. Low, and Richard M. Murray · 2007
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MapReduce: Simplified data processing on large clusters
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T. Hastie, R. Tibshirani, and J. H. Friedman · 2009
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Convergence of min-sum message-passing for convex optimization
Ciamac C. Moallemi and Benjamin Van Roy · 2010
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HOGWILD!: A lock-free approach to parallelizing stochastic gradient descent
Feng Niu, Benjamin Recht, Christopher Ré, and Stephen J. Wright · 2011
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Distributed delayed stochastic optimization
Alekh Agarwal and John C. Duchi · 2012
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Optimal distributed online prediction using mini-batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 2012
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Contractive interference functions and rates of convergence of distributed power control laws
Hamid Reza Feyzmahdavian, Mikael Johansson, and Themistoklis Charalambous · 2012
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An optimal method for stochastic composite optimization
Guanghui Lan · 2012
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Efficiency of coordinate descent methods on huge-scale optimization problems
Yurii Nesterov · 2012
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More effective distributed ML via a stale synchronous parallel parameter server
Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, and Eric P. Xing · 2013
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Distributed delayed proximal gradient methods
Mu Li, David G. Andersen, and Alexander Smola · 2013
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Parameter server for distributed machine learning
Mu Li, Li Zhou, Zichao Yang, Aaron Li, Fei Xia, David G. Andersen, and Alexander Smola · 2013
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Introductory Lectures on Convex Optimization: A Basic Course
Yurii Nesterov · 2013
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A delayed proximal gradient method with linear convergence rate
Hamid Reza Feyzmahdavian, Arda Aytekin, and Mikael Johansson · 2014
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On the convergence rates of asynchronous iterations
Hamid Reza Feyzmahdavian and Mikael Johansson · 2014
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An asynchronous parallel stochastic coordinate descent algorithm
Ji Liu, Stephen J. Wright, Christopher Ré, Victor Bittorf, and Srikrishna Sridhar · 2014
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Incrementally updated gradient methods for constrained and regularized optimization
Paul Tseng and Sangwoon Yun · 2014
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A proximal stochastic gradient method with progressive variance reduction
Lin Xiao and Tong Zhang · 2014
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Convex Optimization Algorithms
Dimitri P. Bertsekas · 2015
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Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2017
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Asynchronous coordinate descent under more realistic assumption
Tao Sun, Robert Hannah, and Wotao Yin · 2017
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Improved asynchronous parallel optimization analysis for stochastic incremental methods
Rémi Leblond, Fabian Pedregosa, and Simon Lacoste-Julien · 2018
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Global convergence rate of proximal incremental aggregated gradient methods
Nuri Denizcan Vanli, Mert Gürbüzbalaban, and Asuman Ozdaglar · 2018
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SGD: General analysis and improved rates
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, and Peter Richtárik · 2019
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Linear convergence of first order methods for non-strongly convex optimization
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Parallel and Distributed Computation: Numerical Methods
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Accelerated, parallel, and proximal coordinate descent
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Giraph unchained: Barrierless asynchronous parallel execution in pregel-like graph processing systems
Minyang Han and Khuzaima Daudjee · 2015
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Xiangru Lian, Yijun Huang, Yuncheng Li, and Ji Liu · 2015
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Asynchronous stochastic coordinate descent: Parallelism and convergence properties
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Coordinate descent algorithms
Stephen J. Wright · 2015
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Ion Necoara, Yurii Nesterov, and Francois Glineur · 2019
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An inertial parallel and asynchronous forward–backward iteration for distributed convex optimization
Giorgos Stathopoulos and Colin N. Jones · 2019
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General proximal incremental aggregated gradient algorithms: Better and novel results under general scheme
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A tight convergence analysis for stochastic gradient descent with delayed updates
Yossi Arjevani, Ohad Shamir, and Nathan Srebro · 2020
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Asynchronous parallel algorithms for nonconvex optimization
Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, and Gesualdo Scutari · 2020
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Fully asynchronous stochastic coordinate descent: a tight lower bound on the parallelism achieving linear speedup
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SCAFFOLD: Stochastic controlled averaging for federated learning
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A distributed flexible delay-tolerant proximal gradient algorithm
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AsyncQVI: Asynchronous-parallel Q-value iteration for discounted markov decision processes with near-optimal sample complexity
Yibo Zeng, Fei Feng, and Wotao Yin · 2020
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Asynchronous distributed learning: Adapting to gradient delays without prior knowledge
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